AI answers that show their sources.
Retrieval-augmented generation (RAG) lets a model answer from your own documents instead of guessing. We build the retrieval layer that finds the right passages, and we track a citation for every answer.
When you need it.
Many apparent model failures are really retrieval failures: the model never saw the right passage. Better retrieval usually fixes more than a bigger model.
- Answers sound right but cite nothingNobody can check where an answer came from, so nobody trusts it.
- The right document exists, but the AI misses itSearch returns near-misses: the wrong version, the wrong section, the wrong customer.
- Long documents get cut up badlyTables, headings and clauses are split mid-thought, so the context the model gets makes no sense.
- Quality changes and nobody knows whyA new document set or a model upgrade quietly makes answers worse.
How it works.
The shape of what we build, from your data to the people who use it.
- Your documentsWikis, PDFs, tickets, contracts
- Chunk & indexSplits that respect structure
- Hybrid searchKeyword + vector
- RerankBest passages first
- Answer + citationsEvery claim points to a source
What we build.
The parts of a production system, each one tested and handed over.
Ingestion and chunking
Pipelines that split documents along their own structure (headings, sections, tables) and keep the metadata you need to filter by.
Hybrid retrieval
Keyword search (BM25) and vector search together, so exact terms and meaning both count.
Reranking
A second pass that orders candidate passages by relevance before the model sees them.
Citation tracking
Every answer carries links to the passages it used, so people can check it.
Access-aware search
Results filtered by who is asking, so people only see what they are allowed to.
Retrieval evals
A test set of real questions with the passages that should be found, run on every change.
What you get.
- A retrieval service connected to your document sources
- Answers with citations people can open and check
- An eval set measuring how often the right passage is found
- Dashboards for quality, latency and cost
- A runbook for adding new document sources
How we run it.
Start with a short discovery sprint and decide with real evidence.
- Step 1
Discovery sprint
2 weeks
We map the problem, look at your data and systems, and agree what “good” means as a real eval set.
- Step 2
Build
Working version early
We build the first working version in your environment, with tests and monitoring from the start.
- Step 3
Harden
Before real users
Latency, cost, failure handling and security, then a runbook your team can follow.
- Step 4
Pilot and run
Real users
A pilot with real users and weekly reviews, then ongoing improvement if you want us to stay.
What we build with.
Model- and provider-agnostic: we pick what fits your latency, cost and compliance needs.
- Retrieval
- BM25 keyword searchVector searchRerankersCitation tracking
- Models
- OpenAIAnthropicAWS BedrockGoogle Vertex AIAzure OpenAILlama (self-hosted)Mistral (self-hosted)
Frequently asked questions.
Still unsure? A 30-minute call with a tech lead answers the rest.
Ask us directlyWhat is RAG, in plain words?
The system first searches your documents for the passages that answer the question, then gives only those to the model. The model answers from your content, and you can see which passages it used.
Do we need a vector database?
Usually, but not on its own. Vector search finds meaning; keyword search finds exact terms like part numbers and names. Most good systems use both, then rerank.
Is fine-tuning better than RAG?
For answering from changing documents, usually not. Better retrieval, prompts and evaluation tend to beat fine-tuning; we tell you when fine-tuning is worth it.
How do you know it’s working?
We build an eval set of real questions with the passages that should be found and the answers you’d accept, and run it on every change.
Let’s talk about your project.
Tell us about your team, your stack and the problem you want solved. We’ll come back with how we would approach it.
Response time
Within one business day
Prefer a call?
Headquarters
Charlotte, North Carolina
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